Module 10 · Neural and language models

Module 10: guided construction and mastery

Use this after the foundation bridge and before attempting independent practice; return to the module run instructions afterward. Read, predict, run, change, and explain. The shared runner remains the complete reference; these small steps expose its ideas.

Worked concept

A token model chooses continuations. Greedy decoding chooses a highest-scoring next token at each step; it does not guarantee a globally best answer. Valid JSON and correct classification are separate events. On 100 records, one changed outcome moves the observed rate by one percentage point; uncertainty still depends on the sample and its design.

Predict and construct

Predict the printed values, shape or rejection before running. Use the documented environment. The notebook contains this same runnable construction cell.

from practical_ml_ai.language import parse_label
learning_outputs = ['{"label":"ham"}', '{"label":"spam","extra":1}', 'spam']
learning_parsed = [parse_label(value) for value in learning_outputs]
print(learning_parsed)
assert learning_parsed == [0,None,None]

Complete with less support

Inspect matched_comparison.csv after the real run. Reconcile per-record baseline and LLM outcomes and explain preprocessing, prevalence and sample-size limits. Declare any prompt experiment on validation first.

Debug a plausible misconception

Can 100 percent valid JSON establish a useful classifier? Write a prediction before opening feedback. Include a counterexample or calculation, not only a definition.

Independent transfer

Compare zero-shot instructions, an explicit example and a structured schema prompt on validation with a fixed budget. Record literature sources and unresolved claims.

Minimum mastery evidence

Keep the evidence listed in the study workflow and name the module outcome each item supports. The criteria describe developing, meeting and extending the outcome.